The Rodent Gene Array Program (R-GAP) will focus on the creation of databases that can be used for developing expression QTL (eQTL) maps for mouse and rat brain and for use in extensive studies of expression control elements. The proposed studies will generate a database of gene expression profiles from brains of 75 Rl strains of ISSXILS mice (LXS mice;males and females separately), and integrate ultrahigh density SNP genotyping of the mouse strains. Additionally, in collaboration with the Gene Network, a web-based resource for gene expression analysis, all of our key data for LXS strains will be curated and made available for public use. These valuable new datasets will catalyze eQTL mapping, as well as mapping of QTLs for alcohol-related behaviors currently being acquired in the LXS set. When combined with QTL analysis of complex traits, the generation of eQTLs facilitates the identification of candidate genes for these traits, and R-GAP will exploit this strategy to identify strong candidate genes that contribute to anxiety and alcohol related behaviors in LXS mice, as well as in other strains of mice and rats. The availability of brain gene expression data and eQTL mapping of brain transcriptome will produce a novel and unique resource for use by alcoholism investigators. The data on gene expression which we will derive from large panels of inbred mice will complement data generated from the Rl strains of mice, and is structured to take advantage of whole genome sequencing data being generated by the NIEHS Mouse Resequencing Project. The RGAP database will also feature an integrated neuroinformatics component that provides information and methods that can be used to search for 5'transcriptional control modules that interact with transcription factors located within the eQTLs and epistatic loci. The R-GAP will not only generate data valuable to alcoholism investigators, but it will also provide access to methodology for normalization, statistical analysis and eQTL searches of the generated data. R-GAP will, in essence, act as the "back end" of a data generating and initial screening component which will be used with the GeneNetwork. GeneNetwork will generate the "front end" features to make all data available to the research community at large.